Testing for Connected Cars – How Does It Work?

An article by Maxim Chernyak, Head of Test Automation and Performance Testing Lab at A1QA.

The automotive industry has come a long way from the early fossil fuel-driven vehicles to those including hybrid and fully electric systems, but aside from power-type advancements, car manufacturers have been making leaps in other areas. Today’s developments have seen the advent of connected and self-driven cars, allowing for an unheard-of level of autonomy.

While self-driven cars are only an emerging technology, connected cars have already been on the market for a while. Connected vehicles are fitted with devices which enable them to connect with the external environment. This includes sharing and obtaining information via mobile broadband (3G-5G), Wi-Fi, or satellite communication technology within internet of things (IoT) networks.

However, due to the developing complexity, connected cars require in-depth quality assurance at the software level to verify that they keep up with their suggested functionality levels.

Testing specialists use various methods to ensure the quality and safety of IoT-powered vehicles. Let’s briefly overview the software testing techniques implemented in the vehicle manufacturing industry, but first, we need to look at how data actually travels within and between connected cars.

How Data Is Shared and Utilized in Connected Vehicles

Before diving straight into testing methodologies, it’s vital to understand how data is shared and utilized in connected vehicles. Data collection, sharing, and use is the backbone of how connected car technology operates. It can be divided into five levels:

Vehicle to Infrastructure (V2I)

Vehicle to Vehicle (V2V)

Vehicle to Cloud (V2C)

Vehicle to Pedestrian (V2P)

Vehicle to Everything (V2X)

These systems are complex and require accuracy, which is why various testing methods are applied to ensure car safety, the integrity of human life, and the systems’ reliability. For this, vehicles can be tested in various environments:

In-laboratory testing implies hardware and software testing in a set and controlled environment; for example, engineers may recreate particular weather patterns or road surfaces. Also, a car can be checked for electrical safety, wireless connection, or performance of individual equipment parts.
On-road testing involves testing in extemporary conditions to evaluate such parameters as functionality, safety, and resilience in real-world conditions.
Virtual environment testing also creates real-world conditions by capturing in-field data and turning it into testing scenarios, at the same time monitoring each part of a vehicle.

Mitigating Risks with Connected Car Testing

With connected car technologies rapidly advancing year by year, applying correct testing solutions is critical. These solutions generally relate to two types of systems:

Advanced Driver Assistance Systems (ADAS) use various sensors, cameras, and alarms to monitor the overall condition and road situation, showing the gathered data on a monitor to enhance the driver’s performance on the road.

Internal environment sensors can monitor speed, parking positions, indicators, high beam lights, and other. External environment sensors include lane departure, forward collision, pedestrian collision, and other warnings.

Telematics systems assist drivers in better routing by using GPS navigation and real-time data exchange with the сloud. It can help with driving in various weather conditions and across different terrains as well as adjust schedules, for example, for truck drivers. This technology can also provide feedback on the driver’s road behavior to encourage vehicle operators to improve their driving skills.

ADAS and telematics systems should be of a high standard and quality as they are responsible for vehicle performance and, as a result, for human life. A lack of testing can lead to failures in internal car parts, which can ultimately result in vehicle or wireless connector breakages. This can cause disorientation for the car due to the absence of communication with the outside world.

These systems require bug detection at an early stage of software development, as well as continuous testing to ensure quality throughout the vehicle lifecycle. To prevent damaging consequences, QA engineers use various types of testing, such as:

Performance testing to verify software endurance under extreme workloads, as well as the amount of data it can handle. The speed and amount of data exchanged with the cloud are also among the parameters tested here.
Interoperability testing to check the compatibility of communication protocols between different parts of the system, for example, ensuring that IoT systems can exchange and provide data on vehicle parts.
Security and access control testing to analyze malware attack risks and the possibility of unauthorized access to sensitive data, among other cybersecurity vulnerabilities.
Regression testing to identify and handle bugs that surfaced during code changes. It’s used to ensure impeccable software quality with every update.

Frequently, these types of testing need to be automated to run continuously, especially with the great amounts of data nodes found in IoT networks. Many companies refer to automation testing services to accelerate testing speed, decrease the number of code bugs, and optimize the time it takes to get software products to market, among other sought-out advantages.

Testing for Road Safety

Software testing is one of the disciplines not to be overlooked when stuffing connected cars with intelligent semi-autonomous systems. Ultimately, it not only helps minimize the number of bugs and mitigate software downtime risks but also determines reliability and thus prevents a number of accidents on our roads.

About the author: Maxim Chernyak is Head of Test Automation and Performance Testing Lab at A1QA, an expert in test automation methodologies and tools for functional and nonfunctional testing. Accountable for the education and adoption of state-of-the-art quality engineering practices by QA teams.